Client's Testimonial

"The AI/ML Rapid-Prototype Pod from Developers.dev was exactly what we needed. They took our concept and turned it into a production-ready, high-performance system in under four months. Their expertise in Python, TensorFlow, and secure cloud architecture was top-tier. We saw a 40% reduction in successful fraudulent transactions within the first quarter of deployment. They are the ideal partner for any startup needing to execute on a complex technical vision, fast."

Ben Carter, Founder & CEO.

Client Overview

Our client is a fast-growing Australian FinTech startup that provides a digital payment gateway for online businesses. After securing their Series B funding, their primary challenge was to combat increasingly sophisticated fraudulent transactions, which threatened their merchant relationships and profitability. They needed to move beyond simple rule-based fraud checks to a real-time, AI-driven system, but lacked the in-house data science and MLOps expertise to build it quickly and securely.

Problem

The client's existing fraud detection system was based on a static set of rules that was easy for fraudsters to circumvent. This resulted in high chargeback rates and required significant manual review by their operations team, which was not scalable. They needed a system that could learn from new transaction patterns in real-time to identify and block fraud instantly.

Key Challenges

Real-Time Performance

The fraud detection model had to analyze and score transactions in milliseconds to avoid impacting the user checkout experience.

Data Security & Compliance

The system would handle sensitive financial data, requiring strict adherence to PCI DSS and Australian data privacy regulations.

Model Accuracy

The AI model needed to be highly accurate, minimizing both false positives (blocking legitimate customers) and false negatives (allowing fraud).

Scalability

The platform had to be able to handle a rapidly growing volume of transactions without performance degradation.

Our Solution

Developers.dev deployed a specialized "AI / ML Rapid-Prototype Pod" combined with a "DevSecOps Automation Pod" to deliver an end-to-end solution. We focused on building a secure, scalable, and easily maintainable MLOps pipeline.

🧠 Machine Learning Model Development

Our data scientists used Python, Pandas, and Scikit-learn for data preprocessing and feature engineering. They developed a gradient-boosting model (using XGBoost) and a neural network (using TensorFlow) to identify complex fraud patterns.

⚡ Real-Time Data Ingestion

We used Apache Kafka to create a streaming data pipeline that fed transaction data into the model for real-time scoring.

🔒 Secure & Scalable Infrastructure

The entire solution was built on AWS using a serverless architecture (AWS Lambda for the scoring engine, S3 for data lakes) to ensure massive scalability and cost-efficiency. All data was encrypted at rest and in transit.

⚙️ MLOps Pipeline

We built a CI/CD pipeline specifically for machine learning using GitLab and MLflow. This automated the process of training, validating, and deploying new versions of the model without downtime.

Implementation and Execution

Collaborated with Client

Collaborated with the client's team to define key fraud indicators and data sources.

Model Training

Developed and trained the initial models on historical transaction data.

Secure AWS Infrastructure

Set up the secure AWS infrastructure using Terraform for an infrastructure-as-code approach.

Real-Time Scoring API

Built the real-time scoring API and integrated it into the client's existing payment processing workflow.

Shadow Mode Deployment

Initially deployed the model in a "shadow mode" to monitor its predictions without blocking transactions, allowing for fine-tuning.

Live System Launch

After a successful shadow period, the system went live, with automated alerts and dashboards for the client's operations team.

Positive Outcome

📉 40% Reduction in Fraud

The system immediately outperformed the old rule-based engine, significantly reducing chargeback losses.

⏱️ 80% Decrease in Manual Reviews

The high accuracy of the model freed up the operations team to focus on complex edge cases rather than routine reviews.

🚀 Sub-100ms Response Time

The serverless architecture ensured that the fraud check added negligible latency to the payment process.

🤝 Enhanced Merchant Trust

The ability to offer superior fraud protection became a key competitive differentiator, helping the client win larger merchant accounts.

Why Choose Us

✅ Verifiable Process Maturity

Our structured approach to prototyping and deployment ensured a high-quality outcome.

🛡️ Ironclad Security

We designed the system with PCI DSS compliance at its core.

🤝 Ecosystem of Experts

The project required data scientists, MLOps engineers, and cloud security experts working in concert.

🏡 100% In-House Talent

A stable team ensured consistency and deep understanding of the complex model.

The client owns the highly valuable, custom-trained AI model and all associated code.

🤖 AI-Augmented Delivery

We used AI code assistants to accelerate the development of the data pipeline scripts.

👍 Zero-Risk Talent Guarantee

We had the right specialists available from day one.

📚 Deep Domain Expertise

Our FinTech experience was crucial for understanding the nuances of payment fraud.

🏆 Proven Track Record

This project showcases our ability to deliver cutting-edge AI solutions.

Conclusion

By leveraging Developers.dev's specialized AI and DevSecOps capabilities, the FinTech startup was able to build a core competitive asset in record time. They mitigated a key business risk and established a scalable, intelligent platform to support their aggressive growth trajectory in the competitive payments market.

FinTech Innovation: Building a Secure, AI-Powered Fraud Detection Platform for a High-Growth Payments Startup

Industry Financial Technology (FinTech)